Documento de resultados

Data

#rm(list = ls()) 
#Cargamos base de datos 
load("~/Dropbox/book_authoritarianism/Data/Analisis/ICCS09.Rdata")
load("~/Dropbox/book_authoritarianism/Data/Analisis/ICCS16.Rdata")

Variables

#Data frames with all variables
#2009
dict09 =  iccs09 %>%
  mutate(time          = 2009) %>%
  mutate(country       = COUNTRY) %>%
  mutate(idcountry     = IDCNTRY) %>%
  mutate(idschool       = IDSCHOOL) %>%
  mutate(idstudent     = IDSTUD) %>%
  mutate(idstudent = (time*1000000000000 + idcountry*100000000 + idstudent)) %>% #NEW IDSTUDENT
  #Authoritarianism
  mutate(dicta1         = 5-LS2P02A) %>% #Government leaders to make decisions without consulting anybody
  mutate(dicta2         = 5-LS2P02B) %>% #People in government must enforce their authority even
  mutate(dicta3         = 5-LS2P02C) %>% #People in government lose part of their authority
  mutate(dicta4         = 5-LS2P02D) %>% #People whose opinions are different must be considered its enemies
  mutate(dicta5         = 5-LS2P02E) %>% #The most important opinion of a country should be that of the pres
  mutate(dicta6         = 5-LS2P02F) %>% #It is fair that the government does not comply with the law
  mutate(dicta7         = 5-LS2P03A) %>% #It is fair that the government does not comply with the law
  mutate(dicta8         = 5-LS2P03B) %>% #Concentration of power in one person guarantees order
  mutate(dicta9         = 5-LS2P03C) %>% #If the president does not agree withCongress>, he should dissolve 
  mutate(dicta_safety   = 5-LS2P03D) %>% #DICTATORSHIPS ARE JUSTIFIED WHEN THEY BRING ORDER AND SAFETY
  mutate(dicta_benefits = 5-LS2P03E) %>% #DICTATORSHIPS ARE JUSTIFIED WHEN THEY BRING ECONOMIC BENEFITS
  mutate(dict          = (dicta_safety + dicta_benefits)/2) %>% #MEAN DIC
  #Dummies
  mutate(dicta1_d      = recode(dicta1, "1"=0, "2"=0, "3"=1, "4"=1)) %>%
  mutate(dicta2_d      = recode(dicta2, "1"=0, "2"=0, "3"=1, "4"=1)) %>%
  mutate(dicta3_d      = recode(dicta3, "1"=0, "2"=0, "3"=1, "4"=1)) %>%
  mutate(dicta4_d      = recode(dicta4, "1"=0, "2"=0, "3"=1, "4"=1)) %>%
  mutate(dicta5_d      = recode(dicta5, "1"=0, "2"=0, "3"=1, "4"=1)) %>%
  mutate(dicta6_d      = recode(dicta6, "1"=0, "2"=0, "3"=1, "4"=1)) %>%
  mutate(dicta7_d      = recode(dicta7, "1"=0, "2"=0, "3"=1, "4"=1)) %>%
  mutate(dicta8_d      = recode(dicta8, "1"=0, "2"=0, "3"=1, "4"=1)) %>%
  mutate(dicta9_d      = recode(dicta9, "1"=0, "2"=0, "3"=1, "4"=1)) %>%
  mutate(dicta_saf_d  = recode(dicta_safety, "1"=0, "2"=0, "3"=1, "4"=1)) %>%
  mutate(dicta_ben_d  = recode(dicta_benefits, "1"=0, "2"=0, "3"=1, "4"=1)) %>%
  #Civic Knowledge
  mutate(pv1civ        = PV1CIV) %>%
  mutate(pv2civ        = PV2CIV) %>%
  mutate(pv3civ        = PV3CIV) %>%
  mutate(pv4civ        = PV4CIV) %>%
  mutate(pv5civ        = PV5CIV) %>%
  mutate(civic_knowledge = (pv1civ + pv2civ + pv3civ + pv4civ + pv5civ)/5) %>% #MEAN CIVIC KNOWLEDGE 
  #Independent Variables
  mutate(s_opdisc        = OPDISC) %>%  #OPENNESS IN CLASS DISCUSSION
  mutate(s_hisced        = HISCED) %>%  #HIGHEST PARENTAL EDUCATIONAL LEVEL 
  mutate(univ            = ifelse(s_hisced>3, 1, 0)) %>% #UNIVERSITARY PARENTS
  mutate(s_hisei         = HISEI) %>%   #PARENT'S HIGHEST OCCUPATIONAL STATUS
  mutate(s_homelit       = HOMELIT) %>% #HOME LITERACY
  mutate(s_gender        = SGENDER) %>% #GENDER OF STUDENT
  mutate(s_age           = SAGE) %>%    #AGE STUDENT
  mutate(s_citcon        = CITCON) %>%  #CONVENTIONAL CITIZENSHIP
  mutate(s_citsoc        = CITSOC) %>%  #SOCIAL MOVEMENT REL. CITIZENSHIP
  mutate(s_citeff        = CITEFF) %>%  #CITIZENSHIP SELF-EFFICACY 
  mutate(s_cntatt        = ATTCNT) %>%  #ATTITUDES TOWARDS OWN COUNTRY 
  mutate(s_geneql        = GENEQL) %>%  #ATTITUDES TOWARDS GENDER EQUALITY
  mutate(s_ethrght       = ETHRGHT) %>% #EQUAL RIGHTS FOR ALL ETHNIC GROUPS 
  mutate(l_attviol       = ATTVIOL) %>% #ATTITUDES: USE OF VIOLENCE 
  mutate(l_attdiv        = ATTDIFF) %>% #ATTITUDES: NEIGHBOURHOOD DIVERSITY
  mutate(l_autgov        = AUTGOV) %>%  #AUTHORITARIANISM IN GOVERNMENT 
  mutate(l_attcorr       = ATTCORR) %>% #CORRUPT PRACTICES IN GOVERNMENT
  mutate(l_dislaw        = DISLAW) %>%  #ATTITUDES: DISOBEYING THE LAW
  mutate(l_empclas       = EMPATH) %>%  #EMPATHY TOWARDS CLASSMATES
  mutate(s_poldisc       = POLDISC) %>% #DISCUSSION OF POL. AND SOC. ISSUES
  #TRUST
  mutate(s_intrust       = INTRUST) %>% #TRUST IN CIVIC INSTITUTIONS 
  mutate(nac_gob       = 5 - IS2P27A) %>% #TRUST INSTITUTIONS-NATIONAL GOVERNMENT  
  mutate(local_gob     = 5 - IS2P27B) %>% #TRUST INSTITUTIONS-LOCAL GOVERNMENT 
  mutate(courts        = 5 - IS2P27C) %>% #TRUST INSTITUTIONS-COURTS
  mutate(police        = 5 - IS2P27D) %>% #TRUST INSTITUTIONS-POLICE 
  mutate(pol_parties   = 5 - IS2P27E) %>% #TRUST INSTITUTIONS-POLITICAL PARTIES
  mutate(parliament    = 5 - IS2P27F) %>% #TRUST INSTITUTIONS-PARLIAMENT
  mutate(media         = 5 - IS2P27G) %>% #TRUST INSTITUTIONS-MEDIA
  mutate(ffaa          = 5 - IS2P27H) %>% #TRUST INSTITUTIONS-FFAA 
  mutate(school        = 5 - IS2P27I) %>% #TRUST INSTITUTIONS-SCHOOL
  mutate(unit_nations  = 5 - IS2P27J) %>% #TRUST INSTITUTIONS-UNITED NATIONS 
  mutate(people        = 5 - IS2P27K) %>% #TRUST INSTITUTIONS-PEOPLE
  mutate(social          = NA) %>%   #TRUST INSTITUTIONS-SOCIAL MEDIA
  #Dummies
  mutate(nac_gob_d         = recode(nac_gob, "1"=0, "2"=0, "3"=1, "4"=1)) %>%
  mutate(local_gob_d       = recode(local_gob, "1"=0, "2"=0, "3"=1, "4"=1)) %>%
  mutate(courts_d          = recode(courts, "1"=0, "2"=0, "3"=1, "4"=1)) %>%
  mutate(police_d          = recode(police, "1"=0, "2"=0, "3"=1, "4"=1)) %>%
  mutate(pol_parties_d     = recode(pol_parties, "1"=0, "2"=0, "3"=1, "4"=1)) %>%
  mutate(parliament_d      = recode(parliament, "1"=0, "2"=0, "3"=1, "4"=1)) %>%
  mutate(media_d           = recode(media, "1"=0, "2"=0, "3"=1, "4"=1)) %>%
  mutate(ffaa_d            = recode(ffaa, "1"=0, "2"=0, "3"=1, "4"=1)) %>%
  mutate(school_d          = recode(school, "1"=0, "2"=0, "3"=1, "4"=1)) %>%
  mutate(unit_nations_d    = recode(unit_nations, "1"=0, "2"=0, "3"=1, "4"=1)) %>%
  mutate(people_d          = recode(people, "1"=0, "2"=0, "3"=1, "4"=1)) %>%
  mutate(social_d          = NA) %>%
  #WEITHINGS5 - 
  mutate(totwgts       = TOTWGTS) %>%   #FINAL STUDENT WEIGHT
  mutate(wgtfac1       = WGTFAC1) %>%   #SCHOOL BASE WEIGHT
  mutate(wgtadj1s      = WGTADJ1S) %>%  #SCHOOL WEIGHT ADJUSTMENT-STUDENT STUDY
  mutate(wgtfac2s      = WGTFAC2S) %>%  #CLASS WEIGHT FACTOR
  mutate(wgtadj2s      = WGTADJ2S) %>%  #CLASS WEIGHT ADJUSTMENT
  mutate(wgtadj3s      = WGTADJ3S) %>%  #STUDENT WEIGHT ADJUSTMENT
  mutate(jkzones       = JKZONES) %>%   #JACKKNIFE ZONE - STUDENT STUDY
  mutate(jkreps        = JKREPS)  %>%  #JACKKNIFE REPLICATE CODE
  select(411:497)
 
#2016  
dict16 = iccs16 %>%
  mutate(time            = 2016) %>%
  mutate(country         = COUNTRY) %>%
  mutate(idcountry       = IDCNTRY) %>%
  mutate(idschool        = IDSCHOOL) %>%
  mutate(idstudent       = IDSTUD) %>%
  mutate(idstudent       = (time*100000000000 + idcountry*100000000 + idstudent)) %>% #NEW IDSTUDENT
  #Authoritarianism
  mutate(dicta1         = 5-LS3G01A) %>% #Government leaders to make decisions without consulting anybody
  mutate(dicta2         = 5-LS3G01B) %>% #People in government must enforce their authority even
  mutate(dicta3         = 5-LS3G01C) %>% #People in government lose part of their authority
  mutate(dicta4         = 5-LS3G01D) %>% #People whose opinions are different must be considered its enemies
  mutate(dicta5         = 5-LS3G01E) %>% #The most important opinion of a country should be that of the pres
  mutate(dicta6         = 5-LS3G01F) %>% #It is fair that the government does not comply with the law
  mutate(dicta7         = 5-LS3G02A) %>% #It is fair that the government does not comply with the law
  mutate(dicta8         = 5-LS3G02B) %>% #Concentration of power in one person guarantees order
  mutate(dicta9         = 5-LS3G02C) %>% #If the president does not agree withCongress>, he should dissolve
  mutate(dicta_safety    = 5-LS3G02D) %>% #DICTATORSHIPS ARE JUSTIFIED WHEN THEY BRING ORDER AND SAFETY
  mutate(dicta_benefits  = 5-LS3G02E) %>% #DICTATORSHIPS ARE JUSTIFIED WHEN THEY BRING ECONOMIC BENEFITS
  mutate(dict           = (dicta_safety + dicta_benefits)/2) %>% 
  #Dummies
  mutate(dicta1_d      = recode(dicta1, "1"=0, "2"=0, "3"=1, "4"=1)) %>%
  mutate(dicta2_d      = recode(dicta2, "1"=0, "2"=0, "3"=1, "4"=1)) %>%
  mutate(dicta3_d      = recode(dicta3, "1"=0, "2"=0, "3"=1, "4"=1)) %>%
  mutate(dicta4_d      = recode(dicta4, "1"=0, "2"=0, "3"=1, "4"=1)) %>%
  mutate(dicta5_d      = recode(dicta5, "1"=0, "2"=0, "3"=1, "4"=1)) %>%
  mutate(dicta6_d      = recode(dicta6, "1"=0, "2"=0, "3"=1, "4"=1)) %>%
  mutate(dicta7_d      = recode(dicta7, "1"=0, "2"=0, "3"=1, "4"=1)) %>%
  mutate(dicta8_d      = recode(dicta8, "1"=0, "2"=0, "3"=1, "4"=1)) %>%
  mutate(dicta9_d      = recode(dicta9, "1"=0, "2"=0, "3"=1, "4"=1)) %>%
  mutate(dicta_saf_d  = recode(dicta_safety, "1"=0, "2"=0, "3"=1, "4"=1)) %>%
  mutate(dicta_ben_d  = recode(dicta_benefits, "1"=0, "2"=0, "3"=1, "4"=1)) %>%
  #Civic Knowledge
  mutate(pv1civ          = PV1CIV) %>%
  mutate(pv2civ          = PV2CIV) %>%
  mutate(pv3civ          = PV3CIV) %>%
  mutate(pv4civ          = PV4CIV) %>%
  mutate(pv5civ          = PV5CIV) %>%
  mutate(civic_knowledge = (pv1civ + pv2civ + pv3civ + pv4civ + pv5civ)/5) %>% #MEAN CIVIC KNOWLEDGE 
  #Independent Variables
  mutate(s_opdisc        = S_OPDISC) %>%  #OPENNESS IN CLASS DISCUSSION
  mutate(s_hisced        = S_HISCED) %>%  #HIGHEST PARENTAL EDUCATIONAL LEVEL 
  mutate(univ            = ifelse(s_hisced>3, 1, 0)) %>% #UNIVERSITARY PARENTS
  mutate(s_hisei         = S_HISEI) %>%   #PARENT'S HIGHEST OCCUPATIONAL STATUS
  mutate(s_homelit       = S_HOMLIT) %>%  #HOME LITERACY
  mutate(s_gender        = S_GENDER) %>%  #GENDER OF STUDENT
  mutate(s_age           = S_AGE) %>%     #AGE STUDENT
  mutate(s_citcon        = S_CITCON) %>%  #CONVENTIONAL CITIZENSHIP
  mutate(s_citsoc        = S_CITSOC) %>%  #SOCIAL MOVEMENT REL. CITIZENSHIP
  mutate(s_citeff        = S_CITEFF) %>%  #CITIZENSHIP SELF-EFFICACY 
  mutate(s_cntatt        = S_CNTATT) %>%  #ATTITUDES TOWARDS OWN COUNTRY 
  mutate(s_geneql        = S_GENEQL) %>%  #ATTITUDES TOWARDS GENDER EQUALITY
  mutate(s_ethrght       = S_ETHRGHT) %>% #EQUAL RIGHTS FOR ALL ETHNIC GROUPS 
  mutate(l_attviol       = L_ATTVIOL) %>% #ATTITUDES: USE OF VIOLENCE 
  mutate(l_attdiv        = L_ATTDIV) %>%  #ATTITUDES: NEIGHBOURHOOD DIVERSITY
  mutate(l_autgov        = L_AUTGOV) %>%  #AUTHORITARIANISM IN GOVERNMENT 
  mutate(l_attcorr       = L_ATTCORR) %>% #CORRUPT PRACTICES IN GOVERNMENT
  mutate(l_dislaw        = L_DISLAW) %>%  #ATTITUDES: DISOBEYING THE LAW
  mutate(l_empclas       = L_EMPCLAS) %>% #EMPATHY TOWARDS CLASSMATES
  mutate(s_poldisc       = S_POLDISC) %>% #DISCUSSION OF POL. AND SOC. ISSUES  
  #TRUST
  mutate(s_intrust       = S_INTRUST) %>% #TRUST IN CIVIC INSTITUTIONS 
  mutate(nac_gob         = 5 - IS3G26A) %>%   #TRUST INSTITUTIONS-NATIONAL GOVERNMENT 
  mutate(local_gob       = 5 - IS3G26B) %>%   #TRUST INSTITUTIONS-LOCAL GOVERNMENT 
  mutate(courts          = 5 - IS3G26C) %>%   #TRUST INSTITUTIONS-COURTS
  mutate(police          = 5 - IS3G26D) %>%   #TRUST INSTITUTIONS-POLICE
  mutate(pol_parties     = 5 - IS3G26E) %>%   #TRUST INSTITUTIONS-POLITICAL PARTIES
  mutate(parliament      = 5 - IS3G26F) %>%   #TRUST INSTITUTIONS-PARLIAMENT
  mutate(media           = 5 - IS3G26G) %>%   #TRUST INSTITUTIONS-MEDIA
  mutate(ffaa            = 5 - IS3G26I) %>%   #TRUST INSTITUTIONS-FFAA
  mutate(school          = 5 - IS3G26J) %>%   #TRUST INSTITUTIONS-SCHOOL
  mutate(unit_nations    = 5 - IS3G26K) %>%   #TRUST INSTITUTIONS-UNITED NATIONS
  mutate(people          = 5 - IS3G26L) %>%   #TRUST INSTITUTIONS-PEOPLE
  mutate(social          = 5 - IS3G26H) %>%   #TRUST INSTITUTIONS-SOCIAL MEDIA
  #Dummies
  mutate(nac_gob_d         = recode(nac_gob,     "1"=0, "2"=0, "3"=1, "4"=1)) %>%
  mutate(local_gob_d       = recode(local_gob,   "1"=0, "2"=0, "3"=1, "4"=1)) %>%
  mutate(courts_d          = recode(courts,      "1"=0, "2"=0, "3"=1, "4"=1)) %>%
  mutate(police_d          = recode(police,      "1"=0, "2"=0, "3"=1, "4"=1)) %>%
  mutate(pol_parties_d     = recode(pol_parties, "1"=0, "2"=0, "3"=1, "4"=1)) %>%
  mutate(parliament_d      = recode(parliament,  "1"=0, "2"=0, "3"=1, "4"=1)) %>%
  mutate(media_d           = recode(media,       "1"=0, "2"=0, "3"=1, "4"=1)) %>%
  mutate(ffaa_d            = recode(ffaa,        "1"=0, "2"=0, "3"=1, "4"=1)) %>%
  mutate(school_d          = recode(school,      "1"=0, "2"=0, "3"=1, "4"=1)) %>%
  mutate(unit_nations_d    = recode(unit_nations,"1"=0, "2"=0, "3"=1, "4"=1)) %>%
  mutate(people_d          = recode(people,      "1"=0, "2"=0, "3"=1, "4"=1)) %>%
  mutate(social_d          = recode(social,      "1"=0, "2"=0, "3"=1, "4"=1)) %>%
  #WEITHINGS5 - 
  mutate(totwgts         = TOTWGTS) %>%   #FINAL STUDENT WEIGHT
  mutate(wgtfac1         = WGTFAC1) %>%   #SCHOOL BASE WEIGHT
  mutate(wgtadj1s        = WGTADJ1S) %>%  #SCHOOL WEIGHT ADJUSTMENT-STUDENT STUDY
  mutate(wgtfac2s        = WGTFAC2S) %>%  #CLASS WEIGHT FACTOR
  mutate(wgtadj2s        = WGTADJ2S) %>%  #CLASS WEIGHT ADJUSTMENT
  mutate(wgtadj3s        = WGTADJ3S) %>%  #STUDENT WEIGHT ADJUSTMENT
  mutate(jkzones         = JKZONES)  %>%  #JACKKNIFE ZONE - STUDENT STUDY
  mutate(jkreps          = JKREPS)  %>%  #JACKKNIFE REPLICATE CODE
  select(519:605)
#Merge data
mergeiccs <- full_join(dict09, dict16)

## Joining, by = c("time", "country", "idcountry", "idschool", "idstudent", "dicta1", "dicta2", "dicta3", "dicta4", "dicta5", "dicta6", "dicta7", "dicta8", "dicta9", "dicta_safety", "dicta_benefits", "dict", "dicta1_d", "dicta2_d", "dicta3_d", "dicta4_d", "dicta5_d", "dicta6_d", "dicta7_d", "dicta8_d", "dicta9_d", "dicta_saf_d", "dicta_ben_d", "pv1civ", "pv2civ", "pv3civ", "pv4civ", "pv5civ", "civic_knowledge", "s_opdisc", "s_hisced", "univ", "s_hisei", "s_homelit", "s_gender", "s_age", "s_citcon", "s_citsoc", "s_citeff", "s_cntatt", "s_geneql", "s_ethrght", "l_attviol", "l_attdiv", "l_autgov", "l_attcorr", "l_dislaw", "l_empclas", "s_poldisc", "s_intrust", "nac_gob", "local_gob", "courts", "police", "pol_parties", "parliament", "media", "ffaa", "school", "unit_nations", "people", "social", "nac_gob_d", "local_gob_d", "courts_d", "police_d", "pol_parties_d", "parliament_d", "media_d", "ffaa_d", "school_d", "unit_nations_d", "people_d", "social_d", "totwgts", "wgtfac1", "wgtadj1s", "wgtfac2s", "wgtadj2s", "wgtadj3s", "jkzones", "jkreps")

mergeiccs$ncountry <- countrycode(mergeiccs$country, 'iso3c', 'country.name') 
mergeiccs <- mergeiccs[, c(1:3,88,4:87)]
#All observations with complete row data
mergeiccs_miss <- mergeiccs[complete.cases(mergeiccs),]

Sample Size

#ICCS LA: 2009 - 2016
iccs_count <- mergeiccs %>% 
  group_by(time, ncountry) %>%
  dplyr::summarise(N=n(), man=mean(s_gender, na.rm=T), age=mean(s_age, na.rm=T)) %>%
  arrange(ncountry)
#ID School
iccs_count2 <- mergeiccs %>% 
  group_by(time, ncountry, idschool) %>%
  dplyr::summarise(n=n()) %>%
  dplyr::summarise(s=n()) %>%
  arrange(ncountry)
iccs_count$s <- iccs_count2$s 
rm(iccs_count2)
iccs_count$time <- as.character(iccs_count$time) 

#Table
#print(xtable(iccs_count[, c(2:1,6,3:5)],  caption = "Sample", format="text"), include.rownames=FALSE)
kable(iccs_count, align = c("lcccccc")) %>%
  kable_styling(bootstrap_options = c("striped", "hover"))

time	ncountry	N	man	age	s
2009	Chile	5173	0.5143801	14.17872	177
2016	Chile	5081	0.4928164	14.17268	178
2009	Colombia	6200	0.5352932	14.37904	196
2016	Colombia	5609	0.5229096	14.59167	150
2009	Dominican Republic	4569	0.5468819	14.85543	145
2016	Dominican Republic	3937	0.5128270	14.18576	141
2009	Guatemala	3998	0.4899699	15.51745	145
2009	Mexico	6565	0.5222493	14.08043	215
2016	Mexico	5526	0.5000000	14.03109	213
2009	Paraguay	3391	0.5213801	14.81850	149
2016	Peru	5166	0.4816105	14.03021	206

Institutional Trust

#Design
        mergesvy <-  mergeiccs %>%
        as_survey_design(
        strata = jkzones, 
        weights = totwgts, 
        ids = jkreps, 
        nest = TRUE)

#Adjust and table
mergesvy[["variables"]][["nac_gob_d"]] <- as.character(mergesvy[["variables"]][["nac_gob_d"]])
mergesvy[["variables"]][["police_d"]] <- as.character(mergesvy[["variables"]][["police_d"]])
mergesvy[["variables"]][["pol_parties_d"]] <- as.character(mergesvy[["variables"]][["pol_parties_d"]])
mergesvy[["variables"]][["people_d"]] <- as.character(mergesvy[["variables"]][["people_d"]])

#########################
#National Goberment
#########################
table_freq_01 <- mergesvy %>%
        dplyr::group_by(country, time, nac_gob_d) %>%
        summarize(proportion = survey_mean(,na.rm=TRUE, "ci"))
#Table
#print(xtable(table_freq_01,  caption = "Sample", format="text"), include.rownames=FALSE)
kable(table_freq_01, align = c("lcccccc")) %>%
  kable_styling(bootstrap_options = c("striped", "hover"))

country	time	nac_gob_d	proportion	proportion_low	proportion_upp
CHL	2009	0	0.3486123	0.3291055	0.3681191
CHL	2009	1	0.6513877	0.6318809	0.6708945
CHL	2009	NA	NA	NA	NA
CHL	2016	0	0.5041627	0.4847861	0.5235392
CHL	2016	1	0.4958373	0.4764608	0.5152139
CHL	2016	NA	NA	NA	NA
COL	2009	0	0.3797472	0.3552204	0.4042741
COL	2009	1	0.6202528	0.5957259	0.6447796
COL	2009	NA	NA	NA	NA
COL	2016	0	0.4476026	0.4236968	0.4715084
COL	2016	1	0.5523974	0.5284916	0.5763032
COL	2016	NA	NA	NA	NA
DOM	2009	0	0.2612638	0.2365092	0.2860184
DOM	2009	1	0.7387362	0.7139816	0.7634908
DOM	2009	NA	NA	NA	NA
DOM	2016	0	0.2219649	0.1992088	0.2447210
DOM	2016	1	0.7780351	0.7552790	0.8007912
DOM	2016	NA	NA	NA	NA
GTM	2009	0	0.5469156	0.5200227	0.5738086
GTM	2009	1	0.4530844	0.4261914	0.4799773
GTM	2009	NA	NA	NA	NA
MEX	2009	0	0.4158023	0.3954032	0.4362014
MEX	2009	1	0.5841977	0.5637986	0.6045968
MEX	2009	NA	NA	NA	NA
MEX	2016	0	0.4299568	0.4090000	0.4509136
MEX	2016	1	0.5700432	0.5490864	0.5910000
MEX	2016	NA	NA	NA	NA
PER	2016	0	0.5096989	0.4893269	0.5300710
PER	2016	1	0.4903011	0.4699290	0.5106731
PER	2016	NA	NA	NA	NA
PRY	2009	0	0.3409936	0.3156542	0.3663329
PRY	2009	1	0.6590064	0.6336671	0.6843458
PRY	2009	NA	NA	NA	NA

#########################
#Police
#########################
table_freq_02 <- mergesvy %>%
        dplyr::group_by(country, time, police_d) %>%
        summarize(proportion = survey_mean(,na.rm=TRUE, "ci"))
#Table
#print(xtable(table_freq_02,  caption = "Sample", format="text"), include.rownames=FALSE)
kable(table_freq_02, align = c("lcccccc")) %>%
  kable_styling(bootstrap_options = c("striped", "hover"))

country	time	police_d	proportion	proportion_low	proportion_upp
CHL	2009	0	0.2896503	0.2717131	0.3075874
CHL	2009	1	0.7103497	0.6924126	0.7282869
CHL	2009	NA	NA	NA	NA
CHL	2016	0	0.3551635	0.3366103	0.3737167
CHL	2016	1	0.6448365	0.6262833	0.6633897
CHL	2016	NA	NA	NA	NA
COL	2009	0	0.4507952	0.4293250	0.4722654
COL	2009	1	0.5492048	0.5277346	0.5706750
COL	2009	NA	NA	NA	NA
COL	2016	0	0.5076088	0.4828832	0.5323344
COL	2016	1	0.4923912	0.4676656	0.5171168
COL	2016	NA	NA	NA	NA
DOM	2009	0	0.4369578	0.4107802	0.4631354
DOM	2009	1	0.5630422	0.5368646	0.5892198
DOM	2009	NA	NA	NA	NA
DOM	2016	0	0.4393891	0.4152856	0.4634927
DOM	2016	1	0.5606109	0.5365073	0.5847144
DOM	2016	NA	NA	NA	NA
GTM	2009	0	0.6664363	0.6423384	0.6905341
GTM	2009	1	0.3335637	0.3094659	0.3576616
GTM	2009	NA	NA	NA	NA
MEX	2009	0	0.5699461	0.5528957	0.5869966
MEX	2009	1	0.4300539	0.4130034	0.4471043
MEX	2009	NA	NA	NA	NA
MEX	2016	0	0.5097115	0.4933779	0.5260450
MEX	2016	1	0.4902885	0.4739550	0.5066221
MEX	2016	NA	NA	NA	NA
PER	2016	0	0.4978979	0.4810746	0.5147213
PER	2016	1	0.5021021	0.4852787	0.5189254
PER	2016	NA	NA	NA	NA
PRY	2009	0	0.5494438	0.5270717	0.5718159
PRY	2009	1	0.4505562	0.4281841	0.4729283
PRY	2009	NA	NA	NA	NA

#########################
#Political Parties
#########################
table_freq_03 <- mergesvy %>%
        dplyr::group_by(country, time, pol_parties_d) %>%
        summarize(proportion = survey_mean(,na.rm=TRUE, "ci"))
#Table
#print(xtable(table_freq_03,  caption = "Sample", format="text"), include.rownames=FALSE)
kable(table_freq_03, align = c("lcccccc")) %>%
  kable_styling(bootstrap_options = c("striped", "hover"))

country	time	pol_parties_d	proportion	proportion_low	proportion_upp
CHL	2009	0	0.6550144	0.6346769	0.6753518
CHL	2009	1	0.3449856	0.3246482	0.3653231
CHL	2009	NA	NA	NA	NA
CHL	2016	0	0.6745075	0.6587782	0.6902368
CHL	2016	1	0.3254925	0.3097632	0.3412218
CHL	2016	NA	NA	NA	NA
COL	2009	0	0.6506877	0.6295206	0.6718549
COL	2009	1	0.3493123	0.3281451	0.3704794
COL	2009	NA	NA	NA	NA
COL	2016	0	0.7228073	0.7029239	0.7426906
COL	2016	1	0.2771927	0.2573094	0.2970761
COL	2016	NA	NA	NA	NA
DOM	2009	0	0.4887925	0.4644688	0.5131161
DOM	2009	1	0.5112075	0.4868839	0.5355312
DOM	2009	NA	NA	NA	NA
DOM	2016	0	0.5024324	0.4798019	0.5250629
DOM	2016	1	0.4975676	0.4749371	0.5201981
DOM	2016	NA	NA	NA	NA
GTM	2009	0	0.7368829	0.7169853	0.7567805
GTM	2009	1	0.2631171	0.2432195	0.2830147
GTM	2009	NA	NA	NA	NA
MEX	2009	0	0.6535926	0.6329986	0.6741866
MEX	2009	1	0.3464074	0.3258134	0.3670014
MEX	2009	NA	NA	NA	NA
MEX	2016	0	0.6261329	0.6059780	0.6462878
MEX	2016	1	0.3738671	0.3537122	0.3940220
MEX	2016	NA	NA	NA	NA
PER	2016	0	0.6660155	0.6471337	0.6848973
PER	2016	1	0.3339845	0.3151027	0.3528663
PER	2016	NA	NA	NA	NA
PRY	2009	0	0.6755932	0.6567009	0.6944855
PRY	2009	1	0.3244068	0.3055145	0.3432991
PRY	2009	NA	NA	NA	NA

#########################
#People in general
#########################
table_freq_04 <- mergesvy %>%
        dplyr::group_by(country, time, people_d) %>%
        summarize(proportion = survey_mean(,na.rm=TRUE, "ci"))
#Table
#print(xtable(table_freq_04,  caption = "Sample", format="text"), include.rownames=FALSE)
kable(table_freq_04, align = c("lcccccc")) %>%
  kable_styling(bootstrap_options = c("striped", "hover"))

country	time	people_d	proportion	proportion_low	proportion_upp
CHL	2009	0	0.4825848	0.4652249	0.4999446
CHL	2009	1	0.5174152	0.5000554	0.5347751
CHL	2009	NA	NA	NA	NA
CHL	2016	0	0.5222508	0.5053029	0.5391987
CHL	2016	1	0.4777492	0.4608013	0.4946971
CHL	2016	NA	NA	NA	NA
COL	2009	0	0.5116344	0.4933059	0.5299628
COL	2009	1	0.4883656	0.4700372	0.5066941
COL	2009	NA	NA	NA	NA
COL	2016	0	0.5651447	0.5430788	0.5872106
COL	2016	1	0.4348553	0.4127894	0.4569212
COL	2016	NA	NA	NA	NA
DOM	2009	0	0.3897426	0.3636206	0.4158645
DOM	2009	1	0.6102574	0.5841355	0.6363794
DOM	2009	NA	NA	NA	NA
DOM	2016	0	0.3801493	0.3586919	0.4016067
DOM	2016	1	0.6198507	0.5983933	0.6413081
DOM	2016	NA	NA	NA	NA
GTM	2009	0	0.5258643	0.5041887	0.5475399
GTM	2009	1	0.4741357	0.4524601	0.4958113
GTM	2009	NA	NA	NA	NA
MEX	2009	0	0.5341219	0.5191580	0.5490859
MEX	2009	1	0.4658781	0.4509141	0.4808420
MEX	2009	NA	NA	NA	NA
MEX	2016	0	0.4829065	0.4638750	0.5019381
MEX	2016	1	0.5170935	0.4980619	0.5361250
MEX	2016	NA	NA	NA	NA
PER	2016	0	0.5284377	0.5113651	0.5455104
PER	2016	1	0.4715623	0.4544896	0.4886349
PER	2016	NA	NA	NA	NA
PRY	2009	0	0.4276067	0.4082165	0.4469968
PRY	2009	1	0.5723933	0.5530032	0.5917835
PRY	2009	NA	NA	NA	NA

Support authoritarianism

#Adjust and table
mergesvy[["variables"]][["dicta_ben_d"]] <- as.character(mergesvy[["variables"]][["dicta_ben_d"]])
mergesvy[["variables"]][["dicta_saf_d"]] <- as.character(mergesvy[["variables"]][["dicta_saf_d"]])

#########################
#Benefits
#########################
table_freq_05 <- mergesvy %>%
        dplyr::group_by(country, time, dicta_ben_d) %>%
        summarize(proportion = survey_mean(,na.rm=TRUE, "ci"))
#Table
#print(xtable(table_freq_05,  caption = "Sample", format="text"), include.rownames=FALSE)
kable(table_freq_05, align = c("lcccccc")) %>%
  kable_styling(bootstrap_options = c("striped", "hover"))

country	time	dicta_ben_d	proportion	proportion_low	proportion_upp
CHL	2009	0	0.3599497	0.3400556	0.3798438
CHL	2009	1	0.6400503	0.6201562	0.6599444
CHL	2009	NA	NA	NA	NA
CHL	2016	0	0.4844084	0.4650626	0.5037541
CHL	2016	1	0.5155916	0.4962459	0.5349374
CHL	2016	NA	NA	NA	NA
COL	2009	0	0.3004007	0.2878003	0.3130012
COL	2009	1	0.6995993	0.6869988	0.7121997
COL	2009	NA	NA	NA	NA
COL	2016	0	0.3248039	0.3035032	0.3461047
COL	2016	1	0.6751961	0.6538953	0.6964968
COL	2016	NA	NA	NA	NA
DOM	2009	0	0.3398834	0.3180054	0.3617615
DOM	2009	1	0.6601166	0.6382385	0.6819946
DOM	2009	NA	NA	NA	NA
DOM	2016	0	0.2997769	0.2781407	0.3214131
DOM	2016	1	0.7002231	0.6785869	0.7218593
DOM	2016	NA	NA	NA	NA
GTM	2009	0	0.2540651	0.2357930	0.2723373
GTM	2009	1	0.7459349	0.7276627	0.7642070
GTM	2009	NA	NA	NA	NA
MEX	2009	0	0.3382672	0.3235268	0.3530077
MEX	2009	1	0.6617328	0.6469923	0.6764732
MEX	2009	NA	NA	NA	NA
MEX	2016	0	0.3357496	0.3159656	0.3555335
MEX	2016	1	0.6642504	0.6444665	0.6840344
MEX	2016	NA	NA	NA	NA
PER	2016	0	0.2820944	0.2654873	0.2987014
PER	2016	1	0.7179056	0.7012986	0.7345127
PER	2016	NA	NA	NA	NA
PRY	2009	0	0.3533853	0.3347247	0.3720458
PRY	2009	1	0.6466147	0.6279542	0.6652753
PRY	2009	NA	NA	NA	NA

#########################
#Safety
#########################

table_freq_06 <- mergesvy %>%
        dplyr::group_by(country, time, dicta_saf_d) %>%
        summarize(proportion = survey_mean(,na.rm=TRUE, "ci"))
#Table
#print(xtable(table_freq_05,  caption = "Sample", format="text"), include.rownames=FALSE)
kable(table_freq_06, align = c("lcccccc")) %>%
  kable_styling(bootstrap_options = c("striped", "hover"))

country	time	dicta_saf_d	proportion	proportion_low	proportion_upp
CHL	2009	0	0.3473609	0.3264615	0.3682604
CHL	2009	1	0.6526391	0.6317396	0.6735385
CHL	2009	NA	NA	NA	NA
CHL	2016	0	0.4344875	0.4130932	0.4558818
CHL	2016	1	0.5655125	0.5441182	0.5869068
CHL	2016	NA	NA	NA	NA
COL	2009	0	0.2604319	0.2465840	0.2742798
COL	2009	1	0.7395681	0.7257202	0.7534160
COL	2009	NA	NA	NA	NA
COL	2016	0	0.2731401	0.2575872	0.2886929
COL	2016	1	0.7268599	0.7113071	0.7424128
COL	2016	NA	NA	NA	NA
DOM	2009	0	0.2953395	0.2751511	0.3155278
DOM	2009	1	0.7046605	0.6844722	0.7248489
DOM	2009	NA	NA	NA	NA
DOM	2016	0	0.2653299	0.2474511	0.2832088
DOM	2016	1	0.7346701	0.7167912	0.7525489
DOM	2016	NA	NA	NA	NA
GTM	2009	0	0.2154922	0.1987022	0.2322822
GTM	2009	1	0.7845078	0.7677178	0.8012978
GTM	2009	NA	NA	NA	NA
MEX	2009	0	0.3148958	0.2981412	0.3316504
MEX	2009	1	0.6851042	0.6683496	0.7018588
MEX	2009	NA	NA	NA	NA
MEX	2016	0	0.3291197	0.3098661	0.3483732
MEX	2016	1	0.6708803	0.6516268	0.6901339
MEX	2016	NA	NA	NA	NA
PER	2016	0	0.2281320	0.2112895	0.2449745
PER	2016	1	0.7718680	0.7550255	0.7887105
PER	2016	NA	NA	NA	NA
PRY	2009	0	0.3052552	0.2857836	0.3247268
PRY	2009	1	0.6947448	0.6752732	0.7142164
PRY	2009	NA	NA	NA	NA

Latin American 2009-2016

Support Authoritarianism

#Students’ endorsement of authoritarian government practices
#IRT WLE scores with mean of 10 and standard deviation of 2 within each participating country
dicta <- ggplot(mergeiccs, aes(civic_knowledge, l_autgov, weight=totwgts, linetype = country))
dicta <-    dicta + geom_smooth(method = "lm", alpha = 0.1, color="black", size=0.5) + 
      facet_wrap(~ time) + 
    labs(x = "Civic Knowledge", y = "Support for authoritarian practices", colour = "dark") 
dicta

#Dictatorships are justified when they bring order and safety 
dicta1 <- ggplot(mergeiccs, aes(civic_knowledge, dicta_safety, weight=totwgts, linetype = country))
dicta1 <-    dicta1 + geom_smooth(method = "lm", alpha = 0.1, color="black", size=0.5) + 
      facet_wrap(~ time) +  
    labs(x = "Civic Knowledge", y = "Support to dictatorship (order and safety)", colour = "dark") 
dicta1

#Dictatorships are justified when they bring economic benefits 
dicta2 <- ggplot(mergeiccs, aes(civic_knowledge, dicta_benefits, weight=totwgts, linetype = country))
dicta2 <-    dicta2 + geom_smooth(method = "lm", alpha = 0.1, color="black", size=0.5) + 
      facet_wrap(~ time) +
    labs(x = "Civic Knowledge", y = "Support to dictatorship (economic benefits)", colour = "dark") 
dicta2

#All graphs
library(ggpubr)
png(file = "~/Dropbox/book_authoritarianism/Resultados/graph/g1.png", height = 300)
ggarrange(dicta, ncol = 1, nrow = 1,  common.legend = TRUE, legend="right")
dev.off()

## quartz_off_screen 
##                 2

png(file = "~/Dropbox/book_authoritarianism/Resultados/graph/g2.png", height = 430)
ggarrange(dicta1, dicta2, ncol = 1, nrow = 2,  common.legend = TRUE, legend="right")
dev.off()

## quartz_off_screen 
##                 2

Institutuional Trust

#Students' trust in civic institutions
#IRT WLE scores with mean of 10 and standard deviation of 2 within each participating country
#National Goberment 
#Local Goberment 
#Courts of justice 
#The police 
#National Parliament 

#Trust in civic institutions
trust <- ggplot(mergeiccs, aes(civic_knowledge, s_intrust, weight=totwgts, linetype = country))
trust <-    trust + geom_smooth(method = "lm", alpha = 0.1, color="black", size=0.5) + 
      facet_wrap(~ time) + 
    labs(x = "Civic Knowledge", y = "Students' trust in civic institutions", colour = "dark") 
trust

#Trust in National Goberment 
nac <- ggplot(mergeiccs, aes(civic_knowledge, nac_gob, weight=totwgts, linetype = country))
nac <-    nac + geom_smooth(method = "lm", alpha = 0.1, color="black", size=0.5) + 
      facet_wrap(~ time) +  
    labs(x = "Civic Knowledge", y = "Trust in National Goberment ", colour = "dark") 
nac

#Trust in Local Goberment
local <- ggplot(mergeiccs, aes(civic_knowledge, local_gob, weight=totwgts, linetype = country))
local <-    local + geom_smooth(method = "lm", alpha = 0.1, color="black", size=0.5) + 
      facet_wrap(~ time) +
    labs(x = "Civic Knowledge", y = "Trust in Local Goberment", colour = "dark") 
local

#Trust National Parliament 
parliament <- ggplot(mergeiccs, aes(civic_knowledge, parliament, weight=totwgts, linetype = country))
parliament <-    parliament + geom_smooth(method = "lm", alpha = 0.1, color="black", size=0.5) + 
      facet_wrap(~ time) +
    labs(x = "Civic Knowledge", y = "Trust in National Parliament", colour = "dark") 
parliament

#Political Parties 
parties <- ggplot(mergeiccs, aes(civic_knowledge, pol_parties, weight=totwgts, linetype = country))
parties <-    parties + geom_smooth(method = "lm", alpha = 0.1, color="black", size=0.5) + 
      facet_wrap(~ time) +
    labs(x = "Civic Knowledge", y = "Trust in Political Parties", colour = "dark") 
parties

#Trust Courts of justice 
courts <- ggplot(mergeiccs, aes(civic_knowledge, courts, weight=totwgts, linetype = country))
courts <-    courts + geom_smooth(method = "lm", alpha = 0.1, color="black", size=0.5) + 
      facet_wrap(~ time) +
    labs(x = "Civic Knowledge", y = "Trust in Courts of justice", colour = "dark") 
courts

#Trust The police 
police <- ggplot(mergeiccs, aes(civic_knowledge, police, weight=totwgts, linetype = country))
police <-    police + geom_smooth(method = "lm", alpha = 0.1, color="black", size=0.5) + 
      facet_wrap(~ time) +
    labs(x = "Civic Knowledge", y = "Trust in the Police", colour = "dark") 
police

#Trust people in general 
people <- ggplot(mergeiccs, aes(civic_knowledge, people, weight=totwgts, linetype = country))
people <-    people + geom_smooth(method = "lm", alpha = 0.1, color="black", size=0.5) + 
      facet_wrap(~ time) +
    labs(x = "Civic Knowledge", y = "Trust in People", colour = "dark") 
people

#All graphs
png(file = "~/Dropbox/book_authoritarianism/Resultados/graph/g3.png", height = 300)
ggarrange(trust, ncol = 1, nrow = 1,  common.legend = TRUE, legend="right")
dev.off()

## quartz_off_screen 
##                 2

png(file = "~/Dropbox/book_authoritarianism/Resultados/graph/g4.png", height = 430)
ggarrange(nac, local, ncol = 1, nrow = 2,  common.legend = TRUE, legend="right")
dev.off()

## quartz_off_screen 
##                 2

png(file = "~/Dropbox/book_authoritarianism/Resultados/graph/g5.png", height = 430)
ggarrange(parliament, parties, ncol = 1, nrow = 2,  common.legend = TRUE, legend="right")
dev.off()

## quartz_off_screen 
##                 2

png(file = "~/Dropbox/book_authoritarianism/Resultados/graph/g6.png", height = 430)
ggarrange(courts, police, ncol = 1, nrow = 2,  common.legend = TRUE, legend="right")
dev.off()

## quartz_off_screen 
##                 2

png(file = "~/Dropbox/book_authoritarianism/Resultados/graph/g7.png", height = 300)
ggarrange(people, ncol = 1, nrow = 1,  common.legend = TRUE, legend="right")
dev.off()

## quartz_off_screen 
##                 2

Interaction

#Modelos de regresión
#VD: Trust
#VD: Support authoritarianism
#VI: Trust 
#VC: Nivel educacional de los padres, Libros en la casa, género del estudiante, nivel de discusión política, cohortes

##########################################################3
#OLS
##########################################################3
#VD: Support authoritarianism
m1 <- lm(l_autgov ~ s_intrust + civic_knowledge, data=mergeiccs, w=totwgts)
m2 <- lm(l_autgov ~ s_intrust + civic_knowledge + s_hisced + s_homelit + s_gender + s_poldisc + time, data=mergeiccs, w=totwgts)
m3 <- lm(l_autgov ~ s_intrust*civic_knowledge + s_hisced + s_homelit + s_gender + s_poldisc + time, data=mergeiccs, w=totwgts)

#VD: Trust
m4 <- lm(s_intrust ~ civic_knowledge, data=mergeiccs, w=totwgts)
m5 <- lm(s_intrust ~ civic_knowledge + s_hisced + s_homelit + s_gender + s_poldisc + time, data=mergeiccs, w=totwgts)

#Table
#print(xtable(iccs_count[, c(2:1,6,3:5)],  caption = "Sample", format="text"), include.rownames=FALSE)
#texreg(list(m1, m2, m3, m4, m5), digits = 2)
#screenreg(list(m1, m2, m3, m4, m5), digits = 4)
htmlreg(list(m1, m2, m3),  caption = "VD: Support authoritarianism")

<!DOCTYPE HTML PUBLIC “-//W3C//DTD HTML 4.01 Transitional//EN” “http://www.w3.org/TR/html4/loose.dtd”>

VD: Support authoritarianism

	Model 1	Model 2	Model 3
(Intercept)	75.87***	-178.68***	-180.92***
	(0.32)	(23.00)	(23.01)
s_intrust	0.17***	0.17***	0.26***
	(0.00)	(0.00)	(0.02)
civic_knowledge	-0.08***	-0.08***	-0.07***
	(0.00)	(0.00)	(0.00)
s_hisced		0.03	0.03
		(0.03)	(0.03)
s_homelit		-0.19***	-0.19***
		(0.04)	(0.04)
s_gender		-1.16***	-1.16***
		(0.08)	(0.08)
s_poldisc		-0.02***	-0.02***
		(0.00)	(0.00)
time		0.13***	0.13***
		(0.01)	(0.01)
s_intrust:civic_knowledge			-0.00***
			(0.00)
R2	0.39	0.39	0.39
Adj. R2	0.39	0.39	0.39
Num. obs.	51794	50281	50281
RMSE	97.78	97.69	97.67
p < 0.001, p < 0.01, p < 0.05

htmlreg(list(m4, m5),  caption = "VD: Institutional Trusts")

<!DOCTYPE HTML PUBLIC “-//W3C//DTD HTML 4.01 Transitional//EN” “http://www.w3.org/TR/html4/loose.dtd”>

VD: Institutional Trusts

	Model 1	Model 2
(Intercept)	63.10***	0.07
	(0.26)	(27.35)
civic_knowledge	-0.03***	-0.03***
	(0.00)	(0.00)
s_hisced		0.03
		(0.03)
s_homelit		-0.31***
		(0.04)
s_gender		-1.04***
		(0.09)
s_poldisc		0.12***
		(0.00)
time		0.03*
		(0.01)
R2	0.05	0.07
Adj. R2	0.05	0.07
Num. obs.	51954	50420
RMSE	117.01	116.37
p < 0.001, p < 0.01, p < 0.05

#Plot
library(sjPlot)
png(file = "~/Dropbox/book_authoritarianism/Resultados/graph/g8.png", height = 300)
plot <- plot_model(m3, type = "pred", terms = c("s_intrust[20,80]", "civic_knowledge[200, 300, 400, 500, 600, 700, 800]"), colors = 'bw', axis.title = c("Students' trust in civic institutions", "Support for authoritarian practices"), title = c(""), legend.title = "Civic Knowledge")
dev.off()

quartz_off_screen 2

plot

plot2 <- plot_model(m3, type = "pred", terms = c("s_intrust[20,80]", "civic_knowledge[200, 300, 400, 500, 600, 700, 800]", "time"), colors = 'bw', axis.title = c("Students' trust in civic institutions", "Support for authoritarian practices"), title = c(""), legend.title = "Civic Knowledge")

plot2

OLS by chile

##########################################################3
#OLS
##########################################################3
#Borramos los modelos generados en el apartado anterior
rm(list=(ls()[!ls() %in% ("mergeiccs")]))

chi <- mergeiccs  %>% filter(idcountry == 152) 

#VD: Support authoritarianism
m1 <- lm(l_autgov ~ s_intrust + civic_knowledge, data=chi, w=totwgts)
m2 <- lm(l_autgov ~ s_intrust + civic_knowledge + s_hisced + s_homelit + s_gender + s_poldisc + time, data=chi, w=totwgts)
m3 <- lm(l_autgov ~ s_intrust*civic_knowledge + s_hisced + s_homelit + s_gender + s_poldisc + time, data=chi, w=totwgts)

#VD: Trust
m4 <- lm(s_intrust ~ civic_knowledge, data=chi, w=totwgts)
m5 <- lm(s_intrust ~ civic_knowledge + s_hisced + s_homelit + s_gender + s_poldisc + time, data=chi, w=totwgts)

#Table
#print(xtable(iccs_count[, c(2:1,6,3:5)],  caption = "Sample", format="text"), include.rownames=FALSE)
#texreg(list(m1, m2, m3, m4, m5), digits = 2)
#screenreg(list(m1, m2, m3, m4, m5), digits = 4)
htmlreg(list(m1, m2, m3),  caption = "VD: Support authoritarianism")

<!DOCTYPE HTML PUBLIC “-//W3C//DTD HTML 4.01 Transitional//EN” “http://www.w3.org/TR/html4/loose.dtd”>

VD: Support authoritarianism

	Model 1	Model 2	Model 3
(Intercept)	72.44***	793.05***	792.12***
	(0.71)	(56.41)	(56.36)
s_intrust	0.20***	0.18***	0.37***
	(0.01)	(0.01)	(0.05)
civic_knowledge	-0.07***	-0.07***	-0.05***
	(0.00)	(0.00)	(0.00)
s_hisced		-0.02	-0.02
		(0.09)	(0.09)
s_homelit		-0.14	-0.14
		(0.09)	(0.09)
s_gender		-0.67***	-0.67***
		(0.18)	(0.18)
s_poldisc		0.01	0.01
		(0.01)	(0.01)
time		-0.36***	-0.36***
		(0.03)	(0.03)
s_intrust:civic_knowledge			-0.00***
			(0.00)
R2	0.36	0.38	0.38
Adj. R2	0.36	0.38	0.38
Num. obs.	10071	9855	9855
RMSE	62.81	62.23	62.18
p < 0.001, p < 0.01, p < 0.05

htmlreg(list(m4, m5),  caption = "VD: Institutional Trusts")

<!DOCTYPE HTML PUBLIC “-//W3C//DTD HTML 4.01 Transitional//EN” “http://www.w3.org/TR/html4/loose.dtd”>

VD: Institutional Trusts

	Model 1	Model 2
(Intercept)	56.49***	949.93***
	(0.61)	(64.44)
civic_knowledge	-0.02***	-0.02***
	(0.00)	(0.00)
s_hisced		-0.08
		(0.11)
s_homelit		-0.21*
		(0.10)
s_gender		-0.86***
		(0.21)
s_poldisc		0.13***
		(0.01)
time		-0.45***
		(0.03)
R2	0.02	0.05
Adj. R2	0.02	0.05
Num. obs.	10089	9873
RMSE	73.29	71.94
p < 0.001, p < 0.01, p < 0.05

plot2 <- plot_model(m3, type = "pred", terms = c("s_intrust[20,80]", "civic_knowledge[200, 300, 400, 500, 600, 700, 800]", "time"), colors = 'bw', axis.title = c("Students' trust in civic institutions", "Support for authoritarian practices"), title = c(""), legend.title = "Civic Knowledge")

plot2

OLS by Colombia

##########################################################3
#OLS
##########################################################3
#Borramos los modelos generados en el apartado anterior
rm(list=(ls()[!ls() %in% ("mergeiccs")]))

col <- mergeiccs  %>% filter(idcountry == 170) 

#VD: Support authoritarianism
m1 <- lm(l_autgov ~ s_intrust + civic_knowledge, data=col, w=totwgts)
m2 <- lm(l_autgov ~ s_intrust + civic_knowledge + s_hisced + s_homelit + s_gender + s_poldisc + time, data=col, w=totwgts)
m3 <- lm(l_autgov ~ s_intrust*civic_knowledge + s_hisced + s_homelit + s_gender + s_poldisc + time, data=col, w=totwgts)

#VD: Trust
m4 <- lm(s_intrust ~ civic_knowledge, data=col, w=totwgts)
m5 <- lm(s_intrust ~ civic_knowledge + s_hisced + s_homelit + s_gender + s_poldisc + time, data=col, w=totwgts)

#Table
#print(xtable(iccs_count[, c(2:1,6,3:5)],  caption = "Sample", format="text"), include.rownames=FALSE)
#texreg(list(m1, m2, m3, m4, m5), digits = 2)
#screenreg(list(m1, m2, m3, m4, m5), digits = 4)
htmlreg(list(m1, m2, m3),  caption = "VD: Support authoritarianism")

<!DOCTYPE HTML PUBLIC “-//W3C//DTD HTML 4.01 Transitional//EN” “http://www.w3.org/TR/html4/loose.dtd”>

VD: Support authoritarianism

	Model 1	Model 2	Model 3
(Intercept)	77.16***	-268.35***	-268.65***
	(0.66)	(44.89)	(44.89)
s_intrust	0.10***	0.11***	0.16***
	(0.01)	(0.01)	(0.05)
civic_knowledge	-0.07***	-0.07***	-0.07***
	(0.00)	(0.00)	(0.00)
s_hisced		0.07	0.07
		(0.05)	(0.05)
s_homelit		-0.13	-0.13
		(0.07)	(0.07)
s_gender		-1.28***	-1.28***
		(0.15)	(0.15)
s_poldisc		-0.03***	-0.03***
		(0.01)	(0.01)
time		0.17***	0.17***
		(0.02)	(0.02)
s_intrust:civic_knowledge			-0.00
			(0.00)
R2	0.35	0.36	0.36
Adj. R2	0.35	0.36	0.36
Num. obs.	11233	11038	11038
RMSE	83.19	82.73	82.72
p < 0.001, p < 0.01, p < 0.05

htmlreg(list(m4, m5),  caption = "VD: Institutional Trusts")

<!DOCTYPE HTML PUBLIC “-//W3C//DTD HTML 4.01 Transitional//EN” “http://www.w3.org/TR/html4/loose.dtd”>

VD: Institutional Trusts

	Model 1	Model 2
(Intercept)	62.34***	258.87***
	(0.59)	(55.83)
civic_knowledge	-0.03***	-0.03***
	(0.00)	(0.00)
s_hisced		0.24***
		(0.06)
s_homelit		-0.24**
		(0.09)
s_gender		-1.80***
		(0.18)
s_poldisc		0.16***
		(0.01)
time		-0.10***
		(0.03)
R2	0.04	0.08
Adj. R2	0.04	0.08
Num. obs.	11262	11064
RMSE	105.09	103.12
p < 0.001, p < 0.01, p < 0.05

plot2 <- plot_model(m3, type = "pred", terms = c("s_intrust[20,80]", "civic_knowledge[200, 300, 400, 500, 600, 700, 800]", "time"), colors = 'bw', axis.title = c("Students' trust in civic institutions", "Support for authoritarian practices"), title = c(""), legend.title = "Civic Knowledge")

plot2

OLS by Dominican Republic

##########################################################3
#OLS
##########################################################3
#Borramos los modelos generados en el apartado anterior
rm(list=(ls()[!ls() %in% ("mergeiccs")]))

dom <- mergeiccs  %>% filter(idcountry == 214) 

#VD: Support authoritarianism
m1 <- lm(l_autgov ~ s_intrust + civic_knowledge, data=dom, w=totwgts)
m2 <- lm(l_autgov ~ s_intrust + civic_knowledge + s_hisced + s_homelit + s_gender + s_poldisc + time, data=dom, w=totwgts)
m3 <- lm(l_autgov ~ s_intrust*civic_knowledge + s_hisced + s_homelit + s_gender + s_poldisc + time, data=dom, w=totwgts)

#VD: Trust
m4 <- lm(s_intrust ~ civic_knowledge, data=dom, w=totwgts)
m5 <- lm(s_intrust ~ civic_knowledge + s_hisced + s_homelit + s_gender + s_poldisc + time, data=dom, w=totwgts)

#Table
#print(xtable(iccs_count[, c(2:1,6,3:5)],  caption = "Sample", format="text"), include.rownames=FALSE)
#texreg(list(m1, m2, m3, m4, m5), digits = 2)
#screenreg(list(m1, m2, m3, m4, m5), digits = 4)
htmlreg(list(m1, m2, m3),  caption = "VD: Support authoritarianism")

<!DOCTYPE HTML PUBLIC “-//W3C//DTD HTML 4.01 Transitional//EN” “http://www.w3.org/TR/html4/loose.dtd”>

VD: Support authoritarianism

	Model 1	Model 2	Model 3
(Intercept)	74.97***	-169.07**	-169.51**
	(0.85)	(60.43)	(60.43)
s_intrust	0.13***	0.12***	0.17**
	(0.01)	(0.01)	(0.05)
civic_knowledge	-0.07***	-0.07***	-0.07***
	(0.00)	(0.00)	(0.01)
s_hisced		0.03	0.03
		(0.08)	(0.08)
s_homelit		-0.44***	-0.44***
		(0.10)	(0.10)
s_gender		-0.85***	-0.85***
		(0.21)	(0.21)
s_poldisc		-0.02	-0.02
		(0.01)	(0.01)
time		0.12***	0.12***
		(0.03)	(0.03)
s_intrust:civic_knowledge			-0.00
			(0.00)
R2	0.31	0.32	0.32
Adj. R2	0.31	0.32	0.32
Num. obs.	7117	6655	6655
RMSE	48.31	47.95	47.95
p < 0.001, p < 0.01, p < 0.05

htmlreg(list(m4, m5),  caption = "VD: Institutional Trusts")

<!DOCTYPE HTML PUBLIC “-//W3C//DTD HTML 4.01 Transitional//EN” “http://www.w3.org/TR/html4/loose.dtd”>

VD: Institutional Trusts

	Model 1	Model 2
(Intercept)	71.67***	-228.12**
	(0.75)	(79.93)
civic_knowledge	-0.04***	-0.04***
	(0.00)	(0.00)
s_hisced		-0.12
		(0.10)
s_homelit		-0.17
		(0.13)
s_gender		-1.58***
		(0.27)
s_poldisc		0.09***
		(0.01)
time		0.15***
		(0.04)
R2	0.07	0.09
Adj. R2	0.07	0.09
Num. obs.	7160	6685
RMSE	64.17	63.62
p < 0.001, p < 0.01, p < 0.05

plot2 <- plot_model(m3, type = "pred", terms = c("s_intrust[20,80]", "civic_knowledge[200, 300, 400, 500, 600, 700, 800]", "time"), colors = 'bw', axis.title = c("Students' trust in civic institutions", "Support for authoritarian practices"), title = c(""), legend.title = "Civic Knowledge")

plot2

OLS by Mexico

##########################################################3
#OLS
##########################################################3
#Borramos los modelos generados en el apartado anterior
rm(list=(ls()[!ls() %in% ("mergeiccs")]))

mex <- mergeiccs  %>% filter(idcountry == 484) 

#VD: Support authoritarianism
m1 <- lm(l_autgov ~ s_intrust + civic_knowledge, data=mex, w=totwgts)
m2 <- lm(l_autgov ~ s_intrust + civic_knowledge + s_hisced + s_homelit + s_gender + s_poldisc + time, data=mex, w=totwgts)
m3 <- lm(l_autgov ~ s_intrust*civic_knowledge + s_hisced + s_homelit + s_gender + s_poldisc + time, data=mex, w=totwgts)

#VD: Trust
m4 <- lm(s_intrust ~ civic_knowledge, data=mex, w=totwgts)
m5 <- lm(s_intrust ~ civic_knowledge + s_hisced + s_homelit + s_gender + s_poldisc + time, data=mex, w=totwgts)

#Table
#print(xtable(iccs_count[, c(2:1,6,3:5)],  caption = "Sample", format="text"), include.rownames=FALSE)
#texreg(list(m1, m2, m3, m4, m5), digits = 2)
#screenreg(list(m1, m2, m3, m4, m5), digits = 4)
htmlreg(list(m1, m2, m3),  caption = "VD: Support authoritarianism")

<!DOCTYPE HTML PUBLIC “-//W3C//DTD HTML 4.01 Transitional//EN” “http://www.w3.org/TR/html4/loose.dtd”>

VD: Support authoritarianism

	Model 1	Model 2	Model 3
(Intercept)	77.58***	-267.31***	-276.62***
	(0.71)	(50.95)	(50.97)
s_intrust	0.20***	0.19***	0.39***
	(0.01)	(0.01)	(0.05)
civic_knowledge	-0.08***	-0.08***	-0.06***
	(0.00)	(0.00)	(0.01)
s_hisced		0.03	0.03
		(0.06)	(0.06)
s_homelit		-0.21**	-0.22**
		(0.08)	(0.08)
s_gender		-1.05***	-1.05***
		(0.17)	(0.17)
s_poldisc		-0.02*	-0.02*
		(0.01)	(0.01)
time		0.17***	0.17***
		(0.03)	(0.03)
s_intrust:civic_knowledge			-0.00***
			(0.00)
R2	0.40	0.41	0.41
Adj. R2	0.40	0.41	0.41
Num. obs.	11583	11379	11379
RMSE	166.24	165.07	164.96
p < 0.001, p < 0.01, p < 0.05

htmlreg(list(m4, m5),  caption = "VD: Institutional Trusts")

<!DOCTYPE HTML PUBLIC “-//W3C//DTD HTML 4.01 Transitional//EN” “http://www.w3.org/TR/html4/loose.dtd”>

VD: Institutional Trusts

	Model 1	Model 2
(Intercept)	63.18***	-272.13***
	(0.57)	(58.97)
civic_knowledge	-0.03***	-0.03***
	(0.00)	(0.00)
s_hisced		-0.04
		(0.07)
s_homelit		-0.34***
		(0.09)
s_gender		-0.74***
		(0.20)
s_poldisc		0.10***
		(0.01)
time		0.16***
		(0.03)
R2	0.05	0.06
Adj. R2	0.05	0.06
Num. obs.	11634	11426
RMSE	193.85	191.71
p < 0.001, p < 0.01, p < 0.05

plot2 <- plot_model(m3, type = "pred", terms = c("s_intrust[20,80]", "civic_knowledge[200, 300, 400, 500, 600, 700, 800]", "time"), colors = 'bw', axis.title = c("Students' trust in civic institutions", "Support for authoritarian practices"), title = c(""), legend.title = "Civic Knowledge")

plot2

OLS by Guatemala 2009

##########################################################3
#OLS
##########################################################3
#Borramos los modelos generados en el apartado anterior
rm(list=(ls()[!ls() %in% ("mergeiccs")]))

gtm <- mergeiccs  %>% filter(idcountry == 320) 

#VD: Support authoritarianism
m1 <- lm(l_autgov ~ s_intrust + civic_knowledge, data=gtm, w=totwgts)
m2 <- lm(l_autgov ~ s_intrust + civic_knowledge + s_hisced + s_homelit + s_gender + s_poldisc + time, data=gtm, w=totwgts)
m3 <- lm(l_autgov ~ s_intrust*civic_knowledge + s_hisced + s_homelit + s_gender + s_poldisc + time, data=gtm, w=totwgts)

#VD: Trust
m4 <- lm(s_intrust ~ civic_knowledge, data=gtm, w=totwgts)
m5 <- lm(s_intrust ~ civic_knowledge + s_hisced + s_homelit + s_gender + s_poldisc + time, data=gtm, w=totwgts)

#Table
#print(xtable(iccs_count[, c(2:1,6,3:5)],  caption = "Sample", format="text"), include.rownames=FALSE)
#texreg(list(m1, m2, m3, m4, m5), digits = 2)
#screenreg(list(m1, m2, m3, m4, m5), digits = 4)
htmlreg(list(m1, m2, m3),  caption = "VD: Support authoritarianism")

<!DOCTYPE HTML PUBLIC “-//W3C//DTD HTML 4.01 Transitional//EN” “http://www.w3.org/TR/html4/loose.dtd”>

VD: Support authoritarianism

	Model 1	Model 2	Model 3
(Intercept)	74.49***	75.83***	89.85***
	(1.11)	(1.32)	(3.62)
s_intrust	0.11***	0.11***	-0.19*
	(0.01)	(0.01)	(0.07)
civic_knowledge	-0.07***	-0.07***	-0.10***
	(0.00)	(0.00)	(0.01)
s_hisced		0.23**	0.26***
		(0.08)	(0.08)
s_homelit		-0.13	-0.13
		(0.11)	(0.11)
s_gender		-1.34***	-1.36***
		(0.24)	(0.24)
s_poldisc		-0.00	0.00
		(0.01)	(0.01)
s_intrust:civic_knowledge			0.00***
			(0.00)
R2	0.35	0.36	0.36
Adj. R2	0.35	0.36	0.36
Num. obs.	3798	3684	3684
RMSE	41.28	41.24	41.15
p < 0.001, p < 0.01, p < 0.05

htmlreg(list(m4, m5),  caption = "VD: Institutional Trusts")

<!DOCTYPE HTML PUBLIC “-//W3C//DTD HTML 4.01 Transitional//EN” “http://www.w3.org/TR/html4/loose.dtd”>

VD: Institutional Trusts

	Model 1	Model 2
(Intercept)	65.52***	58.11***
	(0.93)	(1.35)
civic_knowledge	-0.04***	-0.04***
	(0.00)	(0.00)
s_hisced		-0.17
		(0.10)
s_homelit		-0.26
		(0.14)
s_gender		-0.74*
		(0.30)
s_poldisc		0.12***
		(0.02)
R2	0.10	0.12
Adj. R2	0.10	0.12
Num. obs.	3804	3690
RMSE	52.42	51.66
p < 0.001, p < 0.01, p < 0.05

plot2 <- plot_model(m3, type = "pred", terms = c("s_intrust[20,80]", "civic_knowledge[200, 300, 400, 500, 600, 700, 800]", "time"), colors = 'bw', axis.title = c("Students' trust in civic institutions", "Support for authoritarian practices"), title = c(""), legend.title = "Civic Knowledge")

## Warning in predict.lm(model, newdata = fitfram, type = "response", se.fit =
## se, : prediction from a rank-deficient fit may be misleading

plot2

OLS by Paraguay 2009

##########################################################3
#OLS
##########################################################3
#Borramos los modelos generados en el apartado anterior
rm(list=(ls()[!ls() %in% ("mergeiccs")]))

pry <- mergeiccs  %>% filter(idcountry == 604) 

#VD: Support authoritarianism
m1 <- lm(l_autgov ~ s_intrust + civic_knowledge, data=pry, w=totwgts)
m2 <- lm(l_autgov ~ s_intrust + civic_knowledge + s_hisced + s_homelit + s_gender + s_poldisc + time, data=pry, w=totwgts)
m3 <- lm(l_autgov ~ s_intrust*civic_knowledge + s_hisced + s_homelit + s_gender + s_poldisc + time, data=pry, w=totwgts)

#VD: Trust
m4 <- lm(s_intrust ~ civic_knowledge, data=pry, w=totwgts)
m5 <- lm(s_intrust ~ civic_knowledge + s_hisced + s_homelit + s_gender + s_poldisc + time, data=pry, w=totwgts)

#Table
#print(xtable(iccs_count[, c(2:1,6,3:5)],  caption = "Sample", format="text"), include.rownames=FALSE)
#texreg(list(m1, m2, m3, m4, m5), digits = 2)
#screenreg(list(m1, m2, m3, m4, m5), digits = 4)
htmlreg(list(m1, m2, m3),  caption = "VD: Support authoritarianism")

<!DOCTYPE HTML PUBLIC “-//W3C//DTD HTML 4.01 Transitional//EN” “http://www.w3.org/TR/html4/loose.dtd”>

VD: Support authoritarianism

	Model 1	Model 2	Model 3
(Intercept)	69.95***	71.15***	71.25***
	(0.84)	(1.02)	(2.71)
s_intrust	0.10***	0.09***	0.09
	(0.01)	(0.01)	(0.05)
civic_knowledge	-0.05***	-0.06***	-0.06***
	(0.00)	(0.00)	(0.01)
s_hisced		0.35***	0.35***
		(0.08)	(0.08)
s_homelit		-0.04	-0.04
		(0.10)	(0.10)
s_gender		-1.67***	-1.67***
		(0.20)	(0.20)
s_poldisc		-0.00	-0.00
		(0.01)	(0.01)
s_intrust:civic_knowledge			0.00
			(0.00)
R2	0.35	0.36	0.36
Adj. R2	0.35	0.36	0.36
Num. obs.	5034	4914	4914
RMSE	69.71	69.44	69.45
p < 0.001, p < 0.01, p < 0.05

htmlreg(list(m4, m5),  caption = "VD: Institutional Trusts")

<!DOCTYPE HTML PUBLIC “-//W3C//DTD HTML 4.01 Transitional//EN” “http://www.w3.org/TR/html4/loose.dtd”>

VD: Institutional Trusts

	Model 1	Model 2
(Intercept)	63.12***	55.90***
	(0.68)	(1.08)
civic_knowledge	-0.03***	-0.03***
	(0.00)	(0.00)
s_hisced		0.15
		(0.11)
s_homelit		0.19
		(0.14)
s_gender		-1.75***
		(0.27)
s_poldisc		0.14***
		(0.01)
R2	0.09	0.12
Adj. R2	0.09	0.11
Num. obs.	5037	4916
RMSE	92.75	91.50
p < 0.001, p < 0.01, p < 0.05

plot2 <- plot_model(m3, type = "pred", terms = c("s_intrust[20,80]", "civic_knowledge[200, 300, 400, 500, 600, 700, 800]", "time"), colors = 'bw', axis.title = c("Students' trust in civic institutions", "Support for authoritarian practices"), title = c(""), legend.title = "Civic Knowledge")

## Warning in predict.lm(model, newdata = fitfram, type = "response", se.fit =
## se, : prediction from a rank-deficient fit may be misleading

plot2

OLS by Peru 2016

##########################################################3
#OLS
##########################################################3
#Borramos los modelos generados en el apartado anterior
rm(list=(ls()[!ls() %in% ("mergeiccs")]))

per <- mergeiccs  %>% filter(idcountry == 600) 

#VD: Support authoritarianism
m1 <- lm(l_autgov ~ s_intrust + civic_knowledge, data=per, w=totwgts)
m2 <- lm(l_autgov ~ s_intrust + civic_knowledge + s_hisced + s_homelit + s_gender + s_poldisc + time, data=per, w=totwgts)
m3 <- lm(l_autgov ~ s_intrust*civic_knowledge + s_hisced + s_homelit + s_gender + s_poldisc + time, data=per, w=totwgts)

#VD: Trust
m4 <- lm(s_intrust ~ civic_knowledge, data=per, w=totwgts)
m5 <- lm(s_intrust ~ civic_knowledge + s_hisced + s_homelit + s_gender + s_poldisc + time, data=per, w=totwgts)

#Table
#print(xtable(iccs_count[, c(2:1,6,3:5)],  caption = "Sample", format="text"), include.rownames=FALSE)
#texreg(list(m1, m2, m3, m4, m5), digits = 2)
#screenreg(list(m1, m2, m3, m4, m5), digits = 4)
htmlreg(list(m1, m2, m3),  caption = "VD: Support authoritarianism")

<!DOCTYPE HTML PUBLIC “-//W3C//DTD HTML 4.01 Transitional//EN” “http://www.w3.org/TR/html4/loose.dtd”>

VD: Support authoritarianism

	Model 1	Model 2	Model 3
(Intercept)	70.30***	71.54***	76.89***
	(1.19)	(1.40)	(4.16)
s_intrust	0.13***	0.10***	-0.01
	(0.01)	(0.02)	(0.08)
civic_knowledge	-0.06***	-0.06***	-0.07***
	(0.00)	(0.00)	(0.01)
s_hisced		0.15	0.15
		(0.10)	(0.10)
s_homelit		-0.02	-0.01
		(0.14)	(0.14)
s_gender		-1.40***	-1.41***
		(0.28)	(0.28)
s_poldisc		0.01	0.01
		(0.01)	(0.01)
s_intrust:civic_knowledge			0.00
			(0.00)
R2	0.36	0.36	0.36
Adj. R2	0.35	0.36	0.36
Num. obs.	2958	2756	2756
RMSE	36.43	36.54	36.54
p < 0.001, p < 0.01, p < 0.05

htmlreg(list(m4, m5),  caption = "VD: Institutional Trusts")

<!DOCTYPE HTML PUBLIC “-//W3C//DTD HTML 4.01 Transitional//EN” “http://www.w3.org/TR/html4/loose.dtd”>

VD: Institutional Trusts

	Model 1	Model 2
(Intercept)	63.22***	59.21***
	(0.92)	(1.28)
civic_knowledge	-0.03***	-0.03***
	(0.00)	(0.00)
s_hisced		0.05
		(0.12)
s_homelit		0.23
		(0.17)
s_gender		-2.32***
		(0.34)
s_poldisc		0.10***
		(0.02)
R2	0.07	0.10
Adj. R2	0.07	0.10
Num. obs.	2968	2766
RMSE	45.62	44.53
p < 0.001, p < 0.01, p < 0.05

plot2 <- plot_model(m3, type = "pred", terms = c("s_intrust[20,80]", "civic_knowledge[200, 300, 400, 500, 600, 700, 800]", "time"), colors = 'bw', axis.title = c("Students' trust in civic institutions", "Support for authoritarian practices"), title = c(""), legend.title = "Civic Knowledge")

## Warning in predict.lm(model, newdata = fitfram, type = "response", se.fit =
## se, : prediction from a rank-deficient fit may be misleading

plot2